# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("textattack/roberta-base-CoLA")
model = AutoModelForSequenceClassification.from_pretrained("textattack/roberta-base-CoLA")Quick Links
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
TextAttack Model Cardand the glue dataset loaded using the nlp library. The model was fine-tuned
for 5 epochs with a batch size of 32, a learning rate of 2e-05, and a maximum sequence length of 128. Since this was a classification task, the model was trained with a cross-entropy loss function. The best score the model achieved on this task was 0.850431447746884, as measured by the eval set accuracy, found after 1 epoch.
For more information, check out TextAttack on Github.
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="textattack/roberta-base-CoLA")